SoroushVahidi/scidocs huggingface dataset:
Ultrawideband Metasurface Lenses Based on Off-Shifted Opposite Layers
(2016)

Metasurfaces have demonstrated to be a low-cost solution for the development of directive antennas at high frequency. One of the opportunities of metasurfaces is the possibility to produce planar lenses. However, these lenses usually present a narrow band of operation. This limitation on bandwidth is more restrictive when the required range of refractive index is high. Here, we present a novel implementation of metasurfaces with low dispersion that can be employed for the design of ultrawideband planar lenses. The implementation consists of two mirrored metasurfaces that are displaced exactly a half unit cell. This displacement produces two effects: It increases the equivalent refractive index of the fundamental mode and flattens its frequency dependence.
Planning algorithms for s-curve trajectories
(2007)

Although numerous researches on s-curve motion profiles have been carried out, up to date, no systematic investigation on the general model of polynomial s-curve motion profiles is considered. In this paper, the model of polynomial s-curve motion profiles is generalized in a recursive form. Based on that, a general algorithm to design s-curve trajectory with jerk bounded and time-optimal consideration is proposed. In addition, a special strategy for planning s-curve motion profiles using a trigonometric model is also presented. The algorithms are implemented on a linear motor system. Experimental results show the effectiveness and promising application ability of the algorithms in s-curve motion profiling.
Fibroblast Growth Factor Receptor 2 (FGFR2) Mutation Related Syndromic Craniosynostosis
(2017)

Craniosynostosis results from the premature fusion of cranial sutures, with an incidence of 1 in 2,100-2,500 live births. The majority of cases are non-syndromic and involve single suture fusion, whereas syndromic cases often involve complex multiple suture fusion. The fibroblast growth factor receptor 2 (FGFR2) gene is perhaps the most extensively studied gene that is mutated in various craniosynostotic syndromes including Crouzon, Apert, Pfeiffer, Antley-Bixler, Beare-Stevenson cutis gyrata, Jackson-Weiss, Bent Bone Dysplasia, and Seathre-Chotzen-like syndromes. The majority of these mutations are missense mutations that result in constitutive activation of the receptor and downstream molecular pathways. Treatment involves a multidisciplinary approach with ultimate surgical fixation of the cranial deformity to prevent further sequelae. Understanding the molecular mechanisms has allowed for the investigation of different therapeutic agents that can potentially be used to prevent the disorders. Further research efforts are need to better understand screening and effective methods of early intervention and prevention. Herein, the authors provide a comprehensive update on FGFR2-related syndromic craniosynostosis.
EpiFast: a fast algorithm for large scale realistic epidemic simulations on distributed memory systems
(2009)

Large scale realistic epidemic simulations have recently become an increasingly important application of high-performance computing. We propose a parallel algorithm, EpiFast, based on a novel interpretation of the stochastic disease propagation in a contact network. We implement it using a master-slave computation model which allows scalability on distributed memory systems.
 EpiFast runs extremely fast for realistic simulations that involve: (i) large populations consisting of millions of individuals and their heterogeneous details, (ii) dynamic interactions between the disease propagation, the individual behaviors, and the exogenous interventions, as well as (iii) large number of replicated runs necessary for statistically sound estimates about the stochastic epidemic evolution. We find that EpiFast runs several magnitude faster than another comparable simulation tool while delivering similar results.
 EpiFast has been tested on commodity clusters as well as SGI shared memory machines. For a fixed experiment, if given more computing resources, it scales automatically and runs faster. Finally, EpiFast has been used as the major simulation engine in real studies with rather sophisticated settings to evaluate various dynamic interventions and to provide decision support for public health policy makers.
A "Pile" Metaphor for Supporting Casual Organization of Information
(1992)

A user study was conducted to investigate how people deal with the flow of information in their workspaces. Subjects reported that, in an attempt to quickly and informally manage their information, they created piles of documents. Piles were seen as complementary to the folder filing system, which was used for more formal archiving. A new desktop interface element–the pile– was developed and prototyped through an iterative process. The design includes direct manipulation techniques and support for browsing, and goes beyond physical world functionality by providing system assistance for automatic pile construction and reorganization. Preliminary user tests indicate the design is promising and raise issues that will be addressed in future work.
Multiscale Combinatorial Grouping
(2014)

We propose a unified approach for bottom-up hierarchical image segmentation and object candidate generation for recognition, called Multiscale Combinatorial Grouping (MCG). For this purpose, we first develop a fast normalized cuts algorithm. We then propose a high-performance hierarchical segmenter that makes effective use of multiscale information. Finally, we propose a grouping strategy that combines our multiscale regions into highly-accurate object candidates by exploring efficiently their combinatorial space. We conduct extensive experiments on both the BSDS500 and on the PASCAL 2012 segmentation datasets, showing that MCG produces state-of-the-art contours, hierarchical regions and object candidates.
Fast Approximate Energy Minimization via Graph Cuts
(1999)

In this paper we address the problem of minimizing a large class of energy functions that occur in early vision. The major restriction is that the energy function’s smoothness term must only involve pairs of pixels. We propose two algorithms that use graph cuts to compute a local minimum even when very large moves are allowed. The first move we consider is an α-βswap: for a pair of labels α, β, this move exchanges the labels between an arbitrary set of pixels labeled α and another arbitrary set labeled β. Our first algorithm generates a labeling such that there is no swap move that decreases the energy. The second move we consider is an α-expansion: for a label α, this move assigns an arbitrary set of pixels the label α. Our second algorithm, which requires the smoothness term to be a metric, generates a labeling such that there is no expansion move that decreases the energy. Moreover, this solution is within a known factor of the global minimum. We experimentally demonstrate the effectiveness of our approach on image restoration, stereo and motion. 1 Energy minimization in early vision Many early vision problems require estimating some spatially varying quantity (such as intensity or disparity) from noisy measurements. Such quantities tend to be piecewise smooth; they vary smoothly at most points, but change dramatically at object boundaries. Every pixel p ∈ P must be assigned a label in some set L; for motion or stereo, the labels are disparities, while for image restoration they represent intensities. The goal is to find a labeling f that assigns each pixel p ∈ P a label fp ∈ L, where f is both piecewise smooth and consistent with the observed data. These vision problems can be naturally formulated in terms of energy minimization. In this framework, one seeks the labeling f that minimizes the energy E(f) = Esmooth(f) + Edata(f). Here Esmooth measures the extent to which f is not piecewise smooth, while Edata measures the disagreement between f and the observed data. Many different energy functions have been proposed in the literature. The form of Edata is typically
Real-time object classification in 3D point clouds using point feature histograms
(2009)

This paper describes a LIDAR-based perception system for ground robot mobility, consisting of 3D object detection, classification and tracking. The presented system was demonstrated on-board our autonomous ground vehicle MuCAR-3, enabling it to safely navigate in urban traffic-like scenarios as well as in off-road convoy scenarios. The efficiency of our approach stems from the unique combination of 2D and 3D data processing techniques. Whereas fast segmentation of point clouds into objects is done in a 2 D occupancy grid, classifying the objects is done on raw 3D point clouds. For fast object feature extraction, we advocate the use of statistics of local point cloud properties, captured by histograms over point features. In contrast to most existing work on 3D point cloud classification, where real-time operation is often impossible, this combination allows our system to perform in real-time at 0.1s frame-rate.
Latent Fingerprint Enhancement via Multi-Scale Patch Based Sparse Representation
(2015)

Latent fingerprint identification plays an important role for identifying and convicting criminals in law enforcement agencies. Latent fingerprint images are usually of poor quality with unclear ridge structure and various overlapping patterns. Although significant advances have been achieved on developing automated fingerprint identification system, it is still challenging to achieve reliable feature extraction and identification for latent fingerprints due to the poor image quality. Prior to feature extraction, fingerprint enhancement is necessary to suppress various noises, and improve the clarity of ridge structures in latent fingerprints. Motivated by the recent success of sparse representation in image denoising, this paper proposes a latent fingerprint enhancement algorithm by combining the total variation model and multiscale patch-based sparse representation. First, the total variation model is applied to decompose the latent fingerprint into cartoon and texture components. The cartoon component with most of the nonfingerprint patterns is removed as the structured noise, whereas the texture component consisting of the weak latent fingerprint is enhanced in the next stage. Second, we propose a multiscale patch-based sparse representation method for the enhancement of the texture component. Dictionaries are constructed with a set of Gabor elementary functions to capture the characteristics of fingerprint ridge structure, and multiscale patch-based sparse representation is iteratively applied to reconstruct high-quality fingerprint image. The proposed algorithm cannot only remove the overlapping structured noises, but also restore and enhance the corrupted ridge structures. In addition, we present an automatic method to segment the foreground of latent image with the sparse coefficients and orientation coherence. Experimental results and comparisons on NIST SD27 latent fingerprint database are presented to show the effectiveness of the proposed algorithm and its superiority over existing algorithms.
A study on intelligent personalized push notification with user history
(2017)

In mobile application, the push notification is an important function to make users consume an item (eg. movie, novel). If a mobile application sends too many notifications to users without considering their preferences, it can cause them to turn off the notification or even deleting the mobile application. Thus, we develop a framework that selects a set of users who are likely to react to the push notification. To the best of our knowledge, we first apply recommendation techniques (i.e., collaborative filtering and content-based models) to the problem of discovering the target of push notification.
Social media analytics and research test-bed (SMART dashboard)
(2015)

We developed a social media analytics and research testbed (SMART) dashboard for monitoring Twitter messages and tracking the diffusion of information in different cities. SMART dashboard is an online geo-targeted search and analytics tool, including an automatic data processing procedure to help researchers to 1) search tweets in different cities; 2) filter noise (such as removing redundant retweets and using machine learning methods to improve precision); 3) analyze social media data from a spatiotemporal perspective, and 4) visualize social media data in various ways (such as weekly and monthly trends, top URLs, top retweets, top mentions, or top hashtags). By monitoring social messages in geo-targeted cities, we hope that SMART dashboard can assist researchers investigate and monitor various topics, such as flu outbreaks, drug abuse, and Ebola epidemics at the municipal level.
Racing Bib Numbers Recognition
(2012)

Running races, such as marathons, are broadly covered by professional as well as amateur photographers. This leads to a constantly growing number of photos covering a race, making the process of identifying a particular runner in such datasets difficult. Today, such identification is often done manually. In running races, each competitor has an identification number, called the Racing Bib Number (RBN), used to identify that competitor during the race. RBNs are usually printed on a paper or cardboard tag and pined onto the competitor’s T-shirt during the race. We introduce an automatic system that receives a set of natural images taken in running sports events and outputs the participants’ RBN.
An improved Opposition-Based Sine Cosine Algorithm for global optimization
(2017)

Real life optimization problems require techniques that properly explore the search spaces to obtain the best solutions. In this sense, it is common that traditional optimization algorithms fail in local optimal values. The Sine Cosine Algorithms (SCA) has been recently proposed; it is a global optimization approach based on two trigonometric functions. SCA uses the sine and cosine functions to modify a set of candidate solutions; such operators create a balance between exploration and exploitation of the search space. However, like other similar approaches, SCA tends to be stuck into sub-optimal regions that it is reflected in the computational effort required to find the best values. This situation occurs due that the operators used for exploration do not work well to analyze the search space. This paper presents an improved version of SCA that considers the opposition based learning (OBL) as a mechanism for a better exploration of the search space generating more accurate solutions. OBL is a machine learning strategy commonly used to increase the performance of metaheuristic algorithms. OBL considers the opposite position of a solution in the search space. Based on the objective function value, the OBL selects the best element between the original solution and its opposite position; this task increases the accuracy of the optimization process. The hybridization of concepts from different fields is crucial in intelligent and expert systems; it helps to combine the advantages of algorithms to generate more efficient approaches. The proposed method is an example of this combination; it has been tested over several benchmark functions and engineering problems. Such results support the efficacy of the proposed approach to find the optimal solutions in complex search spaces. © 2017 Elsevier Ltd. All rights reserved. e w g t S f t ( fi b M a b
Clustering Algorithms in Biomedical Research: A Review
(2010)

Applications of clustering algorithms in biomedical research are ubiquitous, with typical examples including gene expression data analysis, genomic sequence analysis, biomedical document mining, and MRI image analysis. However, due to the diversity of cluster analysis, the differing terminologies, goals, and assumptions underlying different clustering algorithms can be daunting. Thus, determining the right match between clustering algorithms and biomedical applications has become particularly important. This paper is presented to provide biomedical researchers with an overview of the status quo of clustering algorithms, to illustrate examples of biomedical applications based on cluster analysis, and to help biomedical researchers select the most suitable clustering algorithms for their own applications.
Towards an automated system for short-answer assessment using ontology mapping
(2015)

A key concern for any e-assessment tool (computer assisted assessment) is its efficiency in assessing the learner’s knowledge, skill set and ability. Multiple-choice questions are the most common means of assessment used in e-assessment systems, and are also successful. An efficient e-assessment system should use variety of question types including shortanswers, essays etc. and modes of response to assess learner’s performance. In this paper, we consider the task of assessing short-answer questions. Several researches have been performed on the evaluation and assessment of short-answer questions and many products are deployed to assess the same as part of e-learning systems. We propose an automated system for assessing short-answers using ontology mapping. We also compare our approach with some existing systems and give an overall evaluation of experiment results, which shows that our approach using ontology mapping gives an optimized result.
A Direct Least-Squares (DLS) method for PnP
(2011)

In this work, we present a Direct Least-Squares (DLS) method for computing all solutions of the perspective-n-point camera pose determination (PnP) problem in the general case (n ≥ 3). Specifically, based on the camera measurement equations, we formulate a nonlinear least-squares cost function whose optimality conditions constitute a system of three third-order polynomials. Subsequently, we employ the multiplication matrix to determine all the roots of the system analytically, and hence all minima of the LS, without requiring iterations or an initial guess of the parameters. A key advantage of our method is scalability, since the order of the polynomial system that we solve is independent of the number of points. We compare the performance of our algorithm with the leading PnP approaches, both in simulation and experimentally, and demonstrate that DLS consistently achieves accuracy close to the Maximum-Likelihood Estimator (MLE).
A Soft Robotic Orthosis for Wrist Rehabilitation 1
(2015)

In the United States about 795,000 people suffer from a stroke each year [1]. Of those who survive the stroke, the majority experience paralysis on one side of their body, a condition known as hemiparesis. Physical therapy has been shown to restore functionality of the paretic limbs, especially when done early and often [2]. However, the effectiveness of therapy is limited by the availability of therapists and the amount of practice that patients do on their own. Robot-assisted therapy has been explored as a means of guiding patients through the time-intensive exercise regimes that most therapy techniques prescribe. Several wearable, robotic orthoses for the hand and wrist have been developed and are still being developed today [3]. However, few of these existing solutions allow for any significant range of motion, and those that do only offer one degree of freedom. The assisted degree of freedom is almost always flexion/extension, despite the fact that supination/ pronation is crucial in nearly all activities of daily living. In addition, current devices are often large, heavy, and uncomfortable for the wearer, presenting significant deterrents to practice. This paper presents a soft wearable device for the wrist that provides motion-specific assistance with rehabilitation for hemiparetic stroke patients at home. Unlike conventional robotassisted rehabilitation, this pneumatically actuated orthosis is portable, soft, and lightweight, making it more suitable for use outside of the clinic. In addition, this device supports all the degrees of freedom of the wrist, including supination and pronation, which are critical to many tasks.
Video enhancement using per-pixel virtual exposures
(2005)

We enhance underexposed, low dynamic range videos by adaptively and independently varying the exposure at each photoreceptor in a post-process. This virtual exposure is a dynamic function of both the spatial neighborhood and temporal history at each pixel. Temporal integration enables us to expand the image's dynamic range while simultaneously reducing noise. Our non-linear exposure variation and denoising filters smoothly transition from temporal to spatial for moving scene elements. Our virtual exposure framework also supports temporally coherent per frame tone mapping. Our system outputs restored video sequences with significantly reduced noise, increased exposure time of dark pixels, intact motion, and improved details.
Effects of pomegranate juice consumption on oxidative stress in patients with type 2 diabetes: a single-blind, randomized clinical trial.
(2017)

Increased free radicals production due to hyperglycemia produces oxidative stress in patients with diabetes. Pomegranate juice (PJ) has antioxidant properties. This study was conducted to determine the effects of PJ consumption in oxidative stress in type 2 diabetic patients. This study was a randomized clinical trial performed on 60, 40-65 years old diabetic patients. The patients were randomly allocated either to PJ consumption group or control. Patients in PJ group consumed 200 ml of PJ daily for six weeks. Sex distribution and the mean age were not different between two groups. After six weeks intervention, oxidized LDL and anti-oxidized LDL antibodies decreased and total serum antioxidant capacity and arylesterase activity of paraoxonase increased significantly in the PJ-treated group compared to the control group. Our data have shown that six weeks supplementation of PJ could have favorable effects on oxidative stress in patients with type 2 diabetes (T2D).
MAG: A Multilingual, Knowledge-base Agnostic and Deterministic Entity Linking Approach
(2017)

Entity linking has recently been the subject of a significant body of research. Currently, the best performing approaches rely on trained mono-lingual models. Porting these approaches to other languages is consequently a difficult endeavor as it requires corresponding training data and retraining of the models. We address this drawback by presenting a novel multilingual, knowledge-base agnostic and deterministic approach to entity linking, dubbed MAG. MAG is based on a combination of context-based retrieval on structured knowledge bases and graph algorithms. We evaluate MAG on 23 data sets and in 7 languages. Our results show that the best approach trained on English datasets (PBOH) achieves a micro F-measure that is up to 4 times worse on datasets in other languages. MAG on the other hand achieves state-of-the-art performance on English datasets and reaches a micro F-measure that is up to 0.6 higher than that of PBOH on non-English languages.
A Memory-Network Based Solution for Multivariate Time-Series Forecasting
(2018)

Multivariate time series forecasting is extensively studied throughout the years with ubiquitous applications in areas such as finance, traffic, environment, etc. Still, concerns have been raised on traditional methods for incapable of modeling complex patterns or dependencies lying in real word data. To address such concerns, various deep learning models, mainly Recurrent Neural Network (RNN) based methods, are proposed. Nevertheless, capturing extremely long-term patterns while effectively incorporating information from other variables remains a challenge for time-series forecasting. Furthermore, lack-of-explainability remains one serious drawback for deep neural network models. Inspired by Memory Network proposed for solving the question-answering task, we propose a deep learning based model named Memory Timeseries network (MTNet) for time series forecasting. MTNet consists of a large memory component, three separate encoders, and an autoregressive component to train jointly. Addtionally, the attention mechanism designed enable MTNet to be highly interpretable. We can easily tell which part of the historic data is referenced the most.